Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement

[11] Aboagye EO(2016)Predicting malignant nodules from screening CT scans J Thorac Oncol 11 2120-2164

[12] Adams JE(2016)Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer Radiology 281 947-918

[13] Sung NS(2016)Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer J Clin Oncol 34 2157-889

[14] Crowley WF(2016)Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features Radiology 281 907-5771

[15] Genel M(2016)Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models Radiology 280 880-391

[16] Choi YJ(2016)Large-scale Radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response Clin Cancer Res 22 5765-5264

[17] Chung MS(2016)MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays Radiology 281 382-476

[18] Koo HJ(2016)Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI Clin Cancer Res 22 5256-1697

[19] Park JE(2017)Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC J Thorac Oncol 12 467-137

[20] Yoon HM(2017)Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab Neuro Oncol 19 1688-1007

[11] Aboagye EO(2016)Predicting malignant nodules from screening CT scans J Thorac Oncol 11 2120-2164

[12] Adams JE(2016)Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer Radiology 281 947-918

[13] Sung NS(2016)Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer J Clin Oncol 34 2157-889

[14] Crowley WF(2016)Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features Radiology 281 907-5771

[15] Genel M(2016)Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models Radiology 280 880-391

[16] Choi YJ(2016)Large-scale Radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response Clin Cancer Res 22 5765-5264

[17] Chung MS(2016)MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays Radiology 281 382-476

[18] Koo HJ(2016)Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI Clin Cancer Res 22 5256-1697

[19] Park JE(2017)Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC J Thorac Oncol 12 467-137

[20] Yoon HM(2017)Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab Neuro Oncol 19 1688-1007